Advertisement

An effective real time gender recognition system for smart cameras

  • Vincenzo Carletti
  • Antonio Greco
  • Alessia SaggeseEmail author
  • Mario Vento
Original Research
  • 75 Downloads

Abstract

In recent years we have assisted to a growing interest for embedded vision, due to the availability of low cost hardware systems, effective for energy consumption, flexible for their size at the cost of limited (compared to the server) computing resources. Their use is boosted by the simplicity of their positioning in places where energy or network bandwidth is limited. Smart cameras are digital cameras embedding computer systems able to host video applications; due to the cost and the performance, they are progressively gaining popularity and conquering large amount of the market. Smart cameras are now able to host on board video applications, even if this imposes an heavy reformulation of the algorithms and of the software design so as to make them compliant with the limited CPUs and the small RAM and flash memory (typically of a few megabytes). In this paper we propose a method for gender recognition on video sequences, specifically designed for making it suited to smart cameras; although the algorithm uses very limited resources (in terms of RAM and CPU), it is able to run on smart cameras available today, presenting at the same time an high accuracy on unrestricted videos taken in real environments (malls, shops, etc.).

Keywords

Smart camera Gender recognition Gender recognition from video Real-time Face analysis Video Embedded vision 

Notes

Acknowledgements

This research has been partially supported by A.I. Tech s.r.l. (www.aitech.vision).

References

  1. Al-Waisy AS, Qahwaji R, Ipson S, Al-Fahdawi S (2018) A multimodal deep learning framework using local feature representations for face recognition. Mach Vis Appl 29(1):35–54CrossRefGoogle Scholar
  2. Alexandre LA (2010) Gender recognition: a multiscale decision fusion approach. Pattern Recogn Lett 31(11):1422–1427CrossRefGoogle Scholar
  3. Azarmehr R, Laganiere R, Lee WS, Xu C, Laroche D (2015) Real-time embedded age and gender classification in unconstrained video. In: IEEE conference on computer vision and pattern recognition workshops, pp 57–65Google Scholar
  4. Azzopardi G, Greco A, Vento M (2016a) Gender recognition from face images using a fusion of SVM classifiers. Springer International Publishing, Berlin, pp 533–538Google Scholar
  5. Azzopardi G, Greco A, Vento M (2016b) Gender recognition from face images with trainable cosfire filters. In: IEEE international conference on advanced video and signal-based surveillance (AVSS)Google Scholar
  6. Azzopardi G, Greco A, Saggese A, Vento M (2017) Fast gender recognition in videos using a novel descriptor based on the gradient magnitudes of facial landmarks. In: Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference on, IEEE, pp 1–6Google Scholar
  7. Baluja S, Rowley HA (2007) Boosting sex identification performance. Int J Comput Vis 71(1):111–119CrossRefGoogle Scholar
  8. BDTI (2017) Embedded vision alliance. https://www.embedded-vision.com/
  9. Bekios-Calfa J, Buenaposada JM, Baumela L (2014) Robust gender recognition by exploiting facial attributes dependencies. Pattern Recogn Lett 36:228–234CrossRefGoogle Scholar
  10. BenAbdelkader C, Griffin P (2005) A local region-based approach to gender classi.cation from face images. In: IEEE conference on computer vision and pattern recognition (CVPR’05)—Workshops, IEEE Computer Society, p 52Google Scholar
  11. Benezeth Y, Jodoin PM, Emile B, Laurent H, Rosenberger C (2010) Comparative study of background subtraction algorithms. J Electron Imaging 19(3):033003CrossRefGoogle Scholar
  12. Bruce V, Burton A, Hanna E, Healey P, Mason O, Coombes A, Fright R, Linney A (1993) Sex discrimination: how do we tell the difference between male and female faces? Perception 22(2):131–52CrossRefGoogle Scholar
  13. Carletti V, Del Pizzo L, Percannella G, Vento M (2014) Foreground detection optimization for socs embedded on smart cameras. In: International conference on distributed smart cameras, ACM, pp 31:1–31:5Google Scholar
  14. Carletti V, Foggia P, Greco A, Saggese A, Vento M (2015) Automatic detection of long term parked cars. In: Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on, IEEE, pp 1–6Google Scholar
  15. Castrillón M, Déniz O, Hernández D, Lorenzo J (2011) A comparison of face and facial feature detectors based on the viola-jones general object detection framework. Mach Vis Appl 22(3):481–494Google Scholar
  16. Chen G, Shao Y, Tang C, Jin Z, Zhang J (2018) Deep transformation learning for face recognition in the unconstrained scene. Mach Vis Appl 29:1–11CrossRefGoogle Scholar
  17. DelPizzo L, Foggia P, Greco A, Percannella G, Vento M (2016) Counting people by RGB or depth overhead cameras. Pattern Recogn Lett 81:41–50CrossRefGoogle Scholar
  18. DiLascio R, Foggia P, Percannella G, Saggese A, Vento M (2013) A real time algorithm for people tracking using contextual reasoning. Comput Vis Image Understand 117(8):892–908CrossRefGoogle Scholar
  19. Ehsan S, Clark AF, McDonald-Maier KD et al (2015) Integral images: efficient algorithms for their computation and storage in resource-constrained embedded vision systems. Sensors 15(7):16804–16830CrossRefGoogle Scholar
  20. Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9(12):2170–2179CrossRefGoogle Scholar
  21. Foggia P, Greco A, Saggese A, Vento M (2015) A method for detecting long term left baggage based on heat map. In: VISAPP (2), pp 385–391Google Scholar
  22. Hu C, Arvin F, Xiong C, Yue S (2017) A bio-inspired embedded vision system for autonomous micro-robots: the lgmd case. In: IEEE transactions on cognitive and developmental systemsGoogle Scholar
  23. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and\(< 0.5\) mb model size. arXiv preprint arXiv:160207360
  24. Kairos (2017) Kairos human analytic sdk. https://www.kairos.com/docs/sdk. Accessed 15 Jan 2019
  25. Kushsairy A, Kamaruddin MK, Nasir H, Safie SI, Bakti ZAK, Isa MR, Khan S (2016) Embedded vision: enhancing embedded platform for face detection system. In: IEEE I2MTC, IEEE, pp 1–5Google Scholar
  26. Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. In: IEEE conference on computer vision and pattern recognition workshops, pp 34–42Google Scholar
  27. Luxand (2017) Luxand api. https://www.luxand.com/. Accessed 15 Jan 2019
  28. MicrosoftFace (2017) Microsoft face api. https://dev.projectoxford.ai/docs/services/. Accessed 15 Jan 2019
  29. Murphy-Chutorian E, Trivedi MM (2009) Head pose estimation in computer vision: a survey. IEEE Trans Pattern Anal Mach Intell 31(4):607–626CrossRefGoogle Scholar
  30. Ng CB, Tay YH, Goi BM (2015) A review of facial gender recognition. Pattern Anal Appl 18(4):739–755MathSciNetCrossRefGoogle Scholar
  31. Parkhi OM, Vedaldi A, Zisserman A et al (2015) Deep face recognition. BMVC 1:6Google Scholar
  32. Ranjan R, Patel VM, Chellappa R (2016) Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. arXiv preprint arXiv:160301249
  33. Ren S, Cao X, Wei Y, Sun J (2014) Face alignment at 3000 fps via regressing local binary features. In: IEEE conference on computer vision and pattern recognition, pp 1685–1692Google Scholar
  34. Selvakumar K, Jerome J, Shankar N, Sarathkumar T (2015) Robust embedded vision system for face detection and identification in smart surveillance. Int J Signal Imaging Syst Eng 8(6):356–366CrossRefGoogle Scholar
  35. Shan C (2012) Learning local binary patterns for gender classification on real-world face images. Pattern Recogn Lett 33(4):431–437CrossRefGoogle Scholar
  36. Tapia JE, Perez CA (2013) Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of lbp, intensity, and shape. IEEE Trans Inf Forensics Secur 8(3):488–499CrossRefGoogle Scholar
  37. Velez G, Cortés A, Nieto M, Vélez I, Otaegui O (2015) A reconfigurable embedded vision system for advanced driver assistance. J Real-Time Image Process 10(4):725–739CrossRefGoogle Scholar
  38. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRefGoogle Scholar
  39. Wang N, Gao X, Tao D, Yang H, Li X (2017) Facial feature point detection: a comprehensive survey. Neurocomputing 275:50–65CrossRefGoogle Scholar
  40. van de Wolfshaar J, Karaaba MF, Wiering MA (2015) Deep convolutional neural networks and support vector machines for gender recognition. In: IEEE symposium series on computational intelligence, IEEE, pp 188–195Google Scholar
  41. Zhou E, Cao Z, Yin Q (2015) Naive-deep face recognition: Touching the limit of LFW benchmark or not? CoRR abs/1501.04690, http://arxiv.org/abs/1501.04690. Accessed 15 Jan 2019

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information and Electrical Engineering and Applied MathematicsUniversity of SalernoFiscianoItaly

Personalised recommendations